vidu/start-end-to-video-2.0

이미지를 비디오로

Vidu Start-End-to-Video 2.0 API by Vidu

vidu/start-end-to-video-2.0

Start-end-to-video-2.0

Open and Advanced Large-Scale Video Generative Models.

Vidu Start-End-to-Video 2.0은(는) ShengShu Technology에서 개발한 모델입니다. Atlas Cloud(운영: Atlas Cloud AI LLC)는 해당 모델에 대한 액세스를 제공할 뿐이며 이를 소유하지 않습니다. 모든 상표는 각 소유자에게 귀속됩니다.

입력

프롬프트 *

이미지 *(0/2)

파일을 드래그 앤 드롭하거나 클릭하여 업로드할 수 있습니다

MAX:2

길이

움직임 폭

시드

출력

대기

생성된 비디오가 여기에 표시됩니다

설정을 구성하고 실행을 클릭하여 시작하세요

요청당 $0.075가 소요됩니다. $10로 이 모델을 약 133번 실행할 수 있습니다.

다음으로 할 수 있는 작업:

Seedance 2.0 Kling v3 Vidu Wan2.7

파라미터

코드 예시
import requests
import time

# Step 1: Start video generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "vidu/start-end-to-video-2.0",  # Required. model name
    "seed": 0,  # The random seed to use for the generation
    "images": [
        "https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg",
        "https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"
    ],  # Required. Supports input of two images, with the first uploaded image considered as the start frame and the second image as the end frame. default: ["https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg","https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"]
    "prompt": "the iron man transform into the sport car ",  # Required. Text prompt: A textual description for video generation, with a maximum length of 1500 characters
    "duration": 4,  # The duration of the generated media in seconds. (min: 4). options: 4 | 8
    "movement_amplitude": "auto",  # The movement amplitude of objects in the frame. options: auto | small | medium | large
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] in ["completed", "succeeded"]:
            print("Generated video:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

video_url = check_status()

설치

사용하는 언어에 필요한 패키지를 설치하세요.

pip install requests

인증

모든 API 요청에는 API 키를 통한 인증이 필요합니다. Atlas Cloud 대시보드에서 API 키를 받을 수 있습니다.

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP 헤더

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

API 키를 안전하게 보관하세요

클라이언트 측 코드나 공개 저장소에 API 키를 노출하지 마세요. 대신 환경 변수 또는 백엔드 프록시를 사용하세요.

요청 제출

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

요청 제출

비동기 생성 요청을 제출합니다. API는 상태 확인 및 결과 조회에 사용할 수 있는 예측 ID를 반환합니다.

POST/api/v1/model/generateVideo

요청 본문

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "vidu/start-end-to-video-2.0",
    "prompt": "A beautiful sunset over the ocean with gentle waves"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

응답

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

상태 확인

예측 엔드포인트를 폴링하여 요청의 현재 상태를 확인합니다.

GET/api/v1/model/prediction/{prediction_id}

폴링 예시

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

상태 값

processing요청이 아직 처리 중입니다.

completed생성이 완료되었습니다. 출력을 사용할 수 있습니다.

succeeded생성이 성공했습니다. 출력을 사용할 수 있습니다.

failed생성에 실패했습니다. 오류 필드를 확인하세요.

완료 응답

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.mp4"
    ],
    "metrics": {
      "predict_time": 45.2
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

파일 업로드

Atlas Cloud 스토리지에 파일을 업로드하고 API 요청에 사용할 수 있는 URL을 받습니다. multipart/form-data를 사용하여 업로드합니다.

POST/api/v1/model/uploadMedia

업로드 예시

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

응답

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

입력 Schema

다음 파라미터를 요청 본문에서 사용할 수 있습니다.

전체: 6필수: 3선택: 3

modelstringrequired

model name

Default: "vidu/start-end-to-video-2.0"

seedinteger

The random seed to use for the generation.

Default: 0

imagesarray[string]required

Supports input of two images, with the first uploaded image considered as the start frame and the second image as the end frame. The model will use these provided images to generate the video. For fields that accept images: Only accept 2 images; The pixel density of the start frame and end frame should be similar. The pixel of the start frame divided by the end frame should be between 0.8 and 1.25; Images Assets can be provided via URLs or Base64 encode; You must use one of the following codecs: PNG, JPEG, JPG, WebP; The aspect ratio of the images must be less than 1:4 or 4:1; All images are limited to 50MB; The length of the base64 decode must be under 50MB, and it must include an appropriate content type string.

Default: ["https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg","https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"]Min items: 2Max items: 2

promptstringrequired

Text prompt: A textual description for video generation, with a maximum length of 1500 characters.

Default: "the iron man transform into the sport car "

durationinteger

The duration of the generated media in seconds.

Default: 4Min: 4

movement_amplitudestring

The movement amplitude of objects in the frame. Defaults to auto, accepted value: auto small medium large.

Default: "auto"

autosmallmediumlarge

요청 본문 예시

{
  "model": "vidu/start-end-to-video-2.0",
  "seed": 0,
  "images": [
    "https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg",
    "https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"
  ],
  "prompt": "the iron man transform into the sport car ",
  "duration": 4,
  "movement_amplitude": "auto"
}

출력 Schema

API는 생성된 출력 URL이 포함된 예측 응답을 반환합니다.

idstring

Unique identifier for the prediction, the ID of the prediction to get.

modelstring

Model ID used for the prediction.

statusstring

Status of the task: created, processing, completed, or failed.

outputsarray

Array of URLs to the generated content (empty when status is not completed).

created_atstring

ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).

응답 예시

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.mp4"
  ],
  "metrics": {
    "predict_time": 45.2
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills는 400개 이상의 AI 모델을 AI 코딩 어시스턴트에 직접 통합합니다. 한 번의 명령으로 설치하고 자연어로 이미지, 동영상 생성 및 LLM과 대화할 수 있습니다.

지원 클라이언트

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ 지원 클라이언트

설치

npx skills add AtlasCloudAI/atlas-cloud-skills

API 키 설정

Atlas Cloud 대시보드에서 API 키를 받아 환경 변수로 설정하세요.

export ATLASCLOUD_API_KEY="your-api-key-here"

기능

설치 후 AI 어시스턴트에서 자연어를 사용하여 모든 Atlas Cloud 모델에 접근할 수 있습니다.

이미지 생성Nano Banana 2, Z-Image 등의 모델로 이미지를 생성합니다.

동영상 제작Kling, Vidu, Veo 등으로 텍스트나 이미지에서 동영상을 만듭니다.

LLM 채팅Qwen, DeepSeek 등 대규모 언어 모델과 대화합니다.

미디어 업로드이미지 편집 및 이미지-동영상 변환 워크플로우를 위해 로컬 파일을 업로드합니다.

더 알아보기

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server는 Model Context Protocol을 통해 IDE와 400개 이상의 AI 모델을 연결합니다. MCP 호환 클라이언트에서 사용할 수 있습니다.

지원 클라이언트

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ 지원 클라이언트

설치

npx -y atlascloud-mcp

설정

다음 설정을 IDE의 MCP 설정 파일에 추가하세요.

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

사용 가능한 도구

atlas_generate_image텍스트 프롬프트로 이미지를 생성합니다.

atlas_generate_video텍스트나 이미지로 동영상을 만듭니다.

atlas_chat대규모 언어 모델과 대화합니다.

atlas_list_models400개 이상의 사용 가능한 AI 모델을 탐색합니다.

atlas_quick_generate최적 모델을 자동 선택하여 한 번에 콘텐츠를 생성합니다.

atlas_upload_mediaAPI 워크플로우를 위해 로컬 파일을 업로드합니다.

더 알아보기

github.com/AtlasCloudAI/mcp-server

API 스키마

{
  "info": {
    "title": "AtlasCloud API",
    "version": "1.0.0",
    "description": "The AtlasCloud API."
  },
  "paths": {
    "/api/v1/model/generateVideo": {
      "post": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        },
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          },
          "required": true
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/result/{request_id}": {
      "get": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        },
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "schema": {
              "type": "string",
              "description": "Request ID"
            },
            "required": true
          }
        ]
      },
      "x-api-name": "model_result"
    }
  },
  "openapi": "3.0.0",
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "components": {
    "schemas": {
      "Input": {
        "type": "object",
        "required": [
          "model",
          "prompt",
          "images"
        ],
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "vidu/start-end-to-video-2.0"
          },
          "seed": {
            "type": "integer",
            "default": 0,
            "description": "The random seed to use for the generation."
          },
          "images": {
            "type": "array",
            "items": {
              "type": "string"
            },
            "default": [
              "https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg",
              "https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"
            ],
            "maxItems": 2,
            "minItems": 2,
            "description": "Supports input of two images, with the first uploaded image considered as the start frame and the second image as the end frame. The model will use these provided images to generate the video. For fields that accept images: Only accept 2 images; The pixel density of the start frame and end frame should be similar. The pixel of the start frame divided by the end frame should be between 0.8 and 1.25; Images Assets can be provided via URLs or Base64 encode; You must use one of the following codecs: PNG, JPEG, JPG, WebP; The aspect ratio of the images must be less than 1:4 or 4:1; All images are limited to 50MB; The length of the base64 decode must be under 50MB, and it must include an appropriate content type string."
          },
          "prompt": {
            "type": "string",
            "default": "the iron man transform into the sport car ",
            "description": "Text prompt: A textual description for video generation, with a maximum length of 1500 characters."
          },
          "duration": {
            "enum": [
              4,
              8
            ],
            "step": 4,
            "type": "integer",
            "default": 4,
            "maximun": 8,
            "minimum": 4,
            "description": "The duration of the generated media in seconds."
          },
          "movement_amplitude": {
            "enum": [
              "auto",
              "small",
              "medium",
              "large"
            ],
            "type": "string",
            "default": "auto",
            "description": "The movement amplitude of objects in the frame. Defaults to auto, accepted value: auto small medium large."
          }
        },
        "x-order-properties": [
          "model",
          "images",
          "prompt",
          "duration",
          "movement_amplitude",
          "seed"
        ]
      },
      "PredictionResponse": {
        "type": "object",
        "properties": {
          "id": {
            "type": "string",
            "description": "Unique identifier for the prediction, the ID of the prediction to get."
          },
          "urls": {
            "type": "object",
            "description": "Object containing related API endpoints."
          },
          "model": {
            "type": "string",
            "description": "Model ID used for the prediction."
          },
          "status": {
            "type": "string",
            "description": "Status of the task: created, processing, completed, or failed."
          },
          "outputs": {
            "type": "array",
            "items": {
              "type": "string"
            },
            "description": "Array of URLs to the generated content (empty when status is not completed)."
          },
          "created_at": {
            "type": "string",
            "format": "date-time",
            "description": "ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)."
          }
        }
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  }
}

LLM 친화적 프롬프트 템플릿

# vidu/start-end-to-video-2.0

> Open and Advanced Large-Scale Video Generative Models.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateVideo` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `vidu/start-end-to-video-2.0`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  model name
  - Default: `"vidu/start-end-to-video-2.0"`

- **`images`** (`array[string]`, _required_):
  Supports input of two images, with the first uploaded image considered as the start frame and the second image as the end frame. The model will use these provided images to generate the video. For fields that accept images: Only accept 2 images; The pixel density of the start frame and end frame should be similar. The pixel of the start frame divided by the end frame should be between 0.8 and 1.25; Images Assets can be provided via URLs or Base64 encode; You must use one of the following codecs: PNG, JPEG, JPG, WebP; The aspect ratio of the images must be less than 1:4 or 4:1; All images are limited to 50MB; The length of the base64 decode must be under 50MB, and it must include an appropriate content type string.
  - Default: `["https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg","https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"]`
  - Min items: 2
  - Max items: 2

- **`prompt`** (`string`, _required_):
  Text prompt: A textual description for video generation, with a maximum length of 1500 characters.
  - Default: `"the iron man transform into the sport car "`

- **`duration`** (`integer`, _optional_):
  The duration of the generated media in seconds.
  - Default: `4`
  - Options: 4, 8
  - Min: 4

- **`movement_amplitude`** (`string`, _optional_):
  The movement amplitude of objects in the frame. Defaults to auto, accepted value: auto small medium large.
  - Default: `"auto"`
  - Options: "auto", "small", "medium", "large"

- **`seed`** (`integer`, _optional_):
  The random seed to use for the generation.
  - Default: `0`



**Required Parameters Example**:

```json
{
  "model": "vidu/start-end-to-video-2.0",
  "prompt": "the iron man transform into the sport car ",
  "images": [
    "https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg",
    "https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"
  ]
}
```


**Full Example**:

```json
{
  "model": "vidu/start-end-to-video-2.0",
  "images": [
    "https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg",
    "https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"
  ],
  "prompt": "the iron man transform into the sport car ",
  "duration": 4,
  "movement_amplitude": "auto",
  "seed": 0
}
```


### Output Schema

The API returns the following output format:


- **`id`** (`string`, _optional_):
  Unique identifier for the prediction, the ID of the prediction to get.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.

- **`model`** (`string`, _optional_):
  Model ID used for the prediction.

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated content (empty when status is not completed).

- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).



**Example Response**:

```json
{
  "id": "",
  "urls": {},
  "model": "",
  "status": "",
  "outputs": [
    ""
  ],
  "created_at": ""
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateVideo" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "vidu/start-end-to-video-2.0",
  "images": [
    "https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg",
    "https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"
  ],
  "prompt": "the iron man transform into the sport car ",
  "duration": 4,
  "movement_amplitude": "auto",
  "seed": 0
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/vidu/start-end-to-video-2.0)

사용 가능한 예제 없음

로드 중...

Vidu2.0 Start end to Video creates coherent video by adding motion between the start and end frames, and is an effective tool for scene transitions and storytelling.

Key Features

Bi-frame guided synthesis
Strong narrative continuity
Object-aware and human-aware motion interpolation
Adaptive to camera movement and layout shifts

Use Cases

Storyboarding and concept animation
Scene interpolation in long-form content
Instructional visual sequences
Film previsualization

Accelerated Inference

Our accelerated inference approach leverages advanced optimization technology from WaveSpeedAI. This innovative fusion technique significantly reduces computational overhead and latency, enabling rapid image generation without compromising quality. The entire system is designed to efficiently handle large-scale inference tasks while ensuring that real-time applications achieve an optimal balance between speed and accuracy. For further details, please refer to the blog post.

Vidu Start-End-to-Video 2.0 API by Vidu

입력

출력

파라미터

코드 예시

설치

인증

HTTP 헤더

요청 제출

요청 제출

요청 본문

응답

상태 확인

폴링 예시

상태 값

완료 응답

파일 업로드

업로드 예시

응답

입력 Schema

요청 본문 예시

출력 Schema

응답 예시

Atlas Cloud Skills

지원 클라이언트

설치

API 키 설정

기능

MCP Server

지원 클라이언트

설치

설정

사용 가능한 도구

API 스키마

LLM 친화적 프롬프트 템플릿

Key Features

Use Cases

Accelerated Inference

유사한 모델 탐색

Vidu Q3-Mix Reference to Video

Vidu Q3 Reference to Video

Vidu Q3-Pro Start-end-to-video

Vidu Q3-Turbo Image-to-video

Vidu Q3-Turbo Start-end-to-video

Vidu Q3-Turbo Text-to-video

Vidu Q3-Pro Image-to-video

Vidu Q3-Pro Text-to-video

Vidu Reference-to-Video Q1

Vidu Reference-to-Video 2.0

Image-to-video-2.0

Vidu Q2-Turbo Start-end-to-video

Vidu Q2-Pro Start-end-to-video

Vidu Q2-Pro-Fast Start-end-to-video

Vidu Q2 Reference-to-video

Vidu Q2-Pro Reference-to-video

하나의 API로 모든 미디어 AI를.

입력

출력

파라미터

코드 예시

설치

인증

HTTP 헤더

요청 제출

요청 제출

요청 본문

응답

상태 확인

폴링 예시

상태 값

완료 응답

파일 업로드

업로드 예시

응답

입력 Schema

요청 본문 예시

출력 Schema

응답 예시

Atlas Cloud Skills

지원 클라이언트